{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T01:21:03Z","timestamp":1769390463035,"version":"3.49.0"},"reference-count":117,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM122083"],"award-info":[{"award-number":["R01GM122083"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54NS091859"],"award-info":[{"award-number":["U54NS091859"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01NS097206"],"award-info":[{"award-number":["P01NS097206"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572327"],"award-info":[{"award-number":["61572327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.<\/jats:p>","DOI":"10.1093\/bib\/bby110","type":"journal-article","created":{"date-parts":[[2018,10,18]],"date-time":"2018-10-18T19:20:43Z","timestamp":1539890443000},"page":"120-134","source":"Crossref","is-referenced-by-count":17,"title":["A comprehensive review of computational prediction of genome-wide features"],"prefix":"10.1093","volume":"21","author":[{"given":"Tianlei","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Emory University , Atlanta, GA, USA"}]},{"given":"Xiaoqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Shanghai Normal University , Shanghai, China"}]},{"given":"Ben","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University , Atlanta, GA, USA"}]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, Rollins School of Public Health, Emory University , Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-146X","authenticated-orcid":false,"given":"Zhaohui","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University , Atlanta, GA, USA"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University , Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"2023060106352524700_ref1","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1038\/nbt1010-1045","article-title":"The NIH roadmap epigenomics mapping consortium","volume":"28","author":"Bernstein","year":"2010","journal-title":"Nat Biotechnol"},{"key":"2023060106352524700_ref2","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nature14248","article-title":"Integrative analysis of 111 reference human epigenomes","volume":"518","author":"Roadmap Epigenomics Consortium","year":"2015","journal-title":"Nature"},{"key":"2023060106352524700_ref3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","author":"ENCODE Project Consortium","year":"2012","journal-title":"Nature"},{"key":"2023060106352524700_ref4","doi-asserted-by":"crossref","first-page":"D91","DOI":"10.1093\/nar\/gkh012","article-title":"JASPAR: an open-access database for eukaryotic transcription factor binding profiles","volume":"32","author":"Sandelin","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref5","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/nar\/gkg108","article-title":"TRANSFAC: transcriptional regulation, from patterns to profiles","volume":"31","author":"Matys","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref6","doi-asserted-by":"crossref","first-page":"D107","DOI":"10.1093\/nar\/gkm967","article-title":"ORegAnno: an open-access community-driven resource for regulatory annotation","volume":"36","author":"Griffith","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref7","doi-asserted-by":"crossref","first-page":"D54","DOI":"10.1093\/nar\/gkn783","article-title":"The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences","volume":"37","author":"Portales-Casamar","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref8","doi-asserted-by":"crossref","first-page":"D171","DOI":"10.1093\/nar\/gks1221","article-title":"Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODE consortium","volume":"41","author":"Wang","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref9","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1101\/gr.127712.111","article-title":"Sequence and chromatin determinants of cell-type-specific transcription factor binding","volume":"22","author":"Arvey","year":"2012","journal-title":"Genome Res"},{"key":"2023060106352524700_ref10","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1126\/science.aad2257","article-title":"Survey of variation in human transcription factors reveals prevalent DNA binding changes","volume":"351","author":"Barrera","year":"2016","journal-title":"Science"},{"key":"2023060106352524700_ref11","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/ng1966","article-title":"Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome","volume":"39","author":"Heintzman","year":"2007","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref12","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.cell.2008.02.022","article-title":"Dynamic regulation of nucleosome positioning in the human genome","volume":"132","author":"Schones","year":"2008","journal-title":"Cell"},{"key":"2023060106352524700_ref13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1093\/nar\/gkn866","article-title":"High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites","volume":"37","author":"Whitington","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref14","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/ng.545","article-title":"Nucleosome dynamics define transcriptional enhancers","volume":"42","author":"He","year":"2010","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref15","doi-asserted-by":"crossref","first-page":"e89226","DOI":"10.1371\/journal.pone.0089226","article-title":"Transcription factor binding sites prediction based on modified nucleosomes","volume":"9","author":"Talebzadeh","year":"2014","journal-title":"PLoS One"},{"key":"2023060106352524700_ref16","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1093\/bioinformatics\/btq405","article-title":"Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites","volume":"26","author":"Ramsey","year":"2010","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref17","doi-asserted-by":"crossref","first-page":"R7","DOI":"10.1186\/gb-2010-11-1-r7","article-title":"Genome-wide prediction of transcription factor binding sites using an integrated model","volume":"11","author":"Won","year":"2010","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref18","doi-asserted-by":"crossref","first-page":"6789","DOI":"10.1073\/pnas.1204398110","article-title":"Differential principal component analysis of ChIP-seq","volume":"110","author":"Ji","year":"2013","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.molcel.2014.08.016","article-title":"DNase footprint signatures are dictated by factor dynamics and DNA sequence","volume":"56","author":"Sung","year":"2014","journal-title":"Mol Cell"},{"key":"2023060106352524700_ref20","doi-asserted-by":"crossref","first-page":"3143","DOI":"10.1093\/bioinformatics\/btu519","article-title":"Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications","volume":"30","author":"Gusmao","year":"2014","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref21","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1101\/gr.112623.110","article-title":"Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data","volume":"21","author":"Pique-Regi","year":"2011","journal-title":"Genome Res"},{"key":"2023060106352524700_ref22","doi-asserted-by":"crossref","first-page":"11865","DOI":"10.1093\/nar\/gku810","article-title":"Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection","volume":"42","author":"Yardimci","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref23","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nbt.2798","article-title":"Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape","volume":"32","author":"Sherwood","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2023060106352524700_ref24","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1093\/bioinformatics\/btw209","article-title":"Romulus: robust multi-state identification of transcription factor binding sites from DNase-seq data","volume":"32","author":"Jankowski","year":"2016","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref25","doi-asserted-by":"crossref","first-page":"4315","DOI":"10.1093\/nar\/gkx174","article-title":"Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility","volume":"45","author":"Chen","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref26","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1093\/bioinformatics\/btr614","article-title":"Epigenetic priors for identifying active transcription factor binding sites","volume":"28","author":"Cuellar-Partida","year":"2012","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref27","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1093\/bioinformatics\/btw740","article-title":"DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter","volume":"33","author":"Quach","year":"2017","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1186\/s12859-017-1769-7","article-title":"Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility","volume":"18","author":"Liu","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023060106352524700_ref29","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1093\/nar\/gkx905","article-title":"Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes","volume":"46","author":"Kuang","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref30","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/nmeth.2762","article-title":"Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification","volume":"11","author":"He","year":"2014","journal-title":"Nat Methods"},{"key":"2023060106352524700_ref31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nmeth.3772","article-title":"Analysis of computational footprinting methods for DNase sequencing experiments","volume":"13","author":"Gusmao","year":"2016","journal-title":"Nat Methods"},{"key":"2023060106352524700_ref32","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1093\/nar\/gkv151","article-title":"Base-resolution methylation patterns accurately predict transcription factor bindings in vivo","volume":"43","author":"Xu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref33","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1093\/bioinformatics\/btx336","article-title":"DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding","volume":"33","author":"Ma","year":"2017","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref34","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat Biotechnol"},{"key":"2023060106352524700_ref35","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Advances in Neural Information Processing Systems 25 (NIPS 2012),","author":"Krizhevsky"},{"key":"2023060106352524700_ref36","first-page":"151274","article-title":"FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data","author":"Quang","year":"2017","journal-title":"BioRxiv"},{"key":"2023060106352524700_ref37","first-page":"1045","article-title":"INTERSPEECH: recurrent neural network based language model","volume-title":"11th Annual Conference of the International Speech Communication Association,","author":"Mikolov"},{"key":"2023060106352524700_ref38","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nature12787","article-title":"An atlas of active enhancers across human cell types and tissues","volume":"507","author":"Andersson","year":"2014","journal-title":"Nature"},{"key":"2023060106352524700_ref39","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1038\/ng.409","article-title":"H3.3\/H2A.Z double variant-containing nucleosomes mark \u2018nucleosome-free regions' of active promoters and other regulatory regions","volume":"41","author":"Jin","year":"2009","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref40","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1101\/gr.5704207","article-title":"The landscape of histone modifications across 1% of the human genome in five human cell lines","volume":"17","author":"Koch","year":"2007","journal-title":"Genome Res"},{"key":"2023060106352524700_ref41","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1101\/gr.129817.111","article-title":"Chromatin state signatures associated with tissue-specific gene expression and enhancer activity in the embryonic limb","volume":"22","author":"Cotney","year":"2012","journal-title":"Genome Res"},{"key":"2023060106352524700_ref42","doi-asserted-by":"crossref","first-page":"21931","DOI":"10.1073\/pnas.1016071107","article-title":"Histone H3K27ac separates active from poised enhancers and predicts developmental state","volume":"107","author":"Creyghton","year":"2010","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref43","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/nature09692","article-title":"A unique chromatin signature uncovers early developmental enhancers in humans","volume":"470","author":"Rada-Iglesias","year":"2011","journal-title":"Nature"},{"key":"2023060106352524700_ref44","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1038\/nature07730","article-title":"ChIP-seq accurately predicts tissue-specific activity of enhancers","volume":"457","author":"Visel","year":"2009","journal-title":"Nature"},{"key":"2023060106352524700_ref45","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1038\/ng.650","article-title":"ChIP-Seq identification of weakly conserved heart enhancers","volume":"42","author":"Blow","year":"2010","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref46","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.immuni.2010.02.008","article-title":"Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages","volume":"32","author":"Ghisletti","year":"2010","journal-title":"Immunity"},{"key":"2023060106352524700_ref47","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/ng.1006","article-title":"Large-scale discovery of enhancers from human heart tissue","volume":"44","author":"May","year":"2011","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref48","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/nature08531","article-title":"Combinatorial binding predicts spatio-temporal cis-regulatory activity","volume":"462","author":"Zinzen","year":"2009","journal-title":"Nature"},{"key":"2023060106352524700_ref49","doi-asserted-by":"crossref","first-page":"5632","DOI":"10.1073\/pnas.1016959108","article-title":"Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart","volume":"108","author":"He","year":"2011","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref50","doi-asserted-by":"crossref","first-page":"R48","DOI":"10.1186\/gb-2012-13-9-r48","article-title":"Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors","volume":"13","author":"Yip","year":"2012","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref51","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1101\/gr.136838.111","article-title":"Understanding transcriptional regulation by integrative analysis of transcription factor binding data","volume":"22","author":"Cheng","year":"2012","journal-title":"Genome Res"},{"key":"2023060106352524700_ref52","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.cell.2012.07.035","article-title":"Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage","volume":"151","author":"Wamstad","year":"2012","journal-title":"Cell"},{"key":"2023060106352524700_ref53","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cell.2012.08.027","article-title":"A temporal chromatin signature in human embryonic stem cells identifies regulators of cardiac development","volume":"151","author":"Paige","year":"2012","journal-title":"Cell"},{"key":"2023060106352524700_ref54","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1101\/gr.122382.111","article-title":"Epigenetic signatures distinguish multiple classes of enhancers with distinct cellular functions","volume":"21","author":"Zentner","year":"2011","journal-title":"Genome Res"},{"key":"2023060106352524700_ref55","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/ng.1064","article-title":"Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development","volume":"44","author":"Bonn","year":"2012","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref56","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1101\/gr.121905.111","article-title":"Discriminative prediction of mammalian enhancers from DNA sequence","volume":"21","author":"Lee","year":"2011","journal-title":"Genome Res"},{"key":"2023060106352524700_ref57","doi-asserted-by":"crossref","first-page":"e1003711","DOI":"10.1371\/journal.pcbi.1003711","article-title":"Enhanced regulatory sequence prediction using gapped k-mer features","volume":"10","author":"Ghandi","year":"2014","journal-title":"PLoS Comput Biol"},{"key":"2023060106352524700_ref58","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1093\/bioinformatics\/btr704","article-title":"CLARE: Cracking the LAnguage of Regulatory Elements","volume":"28","author":"Taher","year":"2012","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref59","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1093\/bioinformatics\/btv604","article-title":"iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition","volume":"32","author":"Liu","year":"2016","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref60","doi-asserted-by":"crossref","first-page":"38741","DOI":"10.1038\/srep38741","article-title":"EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features","volume":"6","author":"Jia","year":"2016","journal-title":"Sci Rep"},{"key":"2023060106352524700_ref61","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1093\/bioinformatics\/btq248","article-title":"Discover regulatory DNA elements using chromatin signatures and artificial neural network","volume":"26","author":"Firpi","year":"2010","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref62","doi-asserted-by":"crossref","first-page":"e77","DOI":"10.1093\/nar\/gks149","article-title":"Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines","volume":"40","author":"Fernandez","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref63","doi-asserted-by":"crossref","first-page":"e1002968","DOI":"10.1371\/journal.pcbi.1002968","article-title":"RFECS: a random-forest based algorithm for enhancer identification from chromatin state","volume":"9","author":"Rajagopal","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"2023060106352524700_ref64","doi-asserted-by":"crossref","first-page":"e0130622","DOI":"10.1371\/journal.pone.0130622","article-title":"DELTA: a Distal Enhancer Locating Tool based on AdaBoost algorithm and shape features of chromatin modifications","volume":"10","author":"Lu","year":"2015","journal-title":"PLoS One"},{"key":"2023060106352524700_ref65","doi-asserted-by":"crossref","first-page":"e6","DOI":"10.1093\/nar\/gku1058","article-title":"DEEP: a general computational framework for predicting enhancers","volume":"43","author":"Kleftogiannis","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref66","doi-asserted-by":"crossref","first-page":"e1003677","DOI":"10.1371\/journal.pcbi.1003677","article-title":"Integrating diverse datasets improves developmental enhancer prediction","volume":"10","author":"Erwin","year":"2014","journal-title":"PLoS Comput Biol"},{"key":"2023060106352524700_ref67","doi-asserted-by":"crossref","first-page":"28517","DOI":"10.1038\/srep28517","article-title":"PEDLA: predicting enhancers with a deep learning-based algorithmic framework","volume":"6","author":"Liu","year":"2016","journal-title":"Sci Rep"},{"key":"2023060106352524700_ref68","doi-asserted-by":"crossref","first-page":"E1633","DOI":"10.1073\/pnas.1618353114","article-title":"Improved regulatory element prediction based on tissue-specific local epigenomic signatures","volume":"114","author":"He","year":"2017","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref69","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nrg3354","article-title":"DNA methylation: roles in mammalian development","volume":"14","author":"Smith","year":"2013","journal-title":"Nat Rev Genet"},{"key":"2023060106352524700_ref70","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1126\/science.1063852","article-title":"The role of DNA methylation in mammalian epigenetics","volume":"293","author":"Jones","year":"2001","journal-title":"Science"},{"issue":"Suppl 1","key":"2023060106352524700_ref71","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1038\/ncponc0354","article-title":"DNA methylation and gene silencing in cancer","volume":"2","author":"Baylin","year":"2005","journal-title":"Nat Clin Pract Oncol"},{"key":"2023060106352524700_ref72","first-page":"461","article-title":"DNA methylation and cancer","volume":"46","author":"Jones","year":"1986","journal-title":"Cancer Res"},{"key":"2023060106352524700_ref73","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1016\/j.cell.2012.04.027","article-title":"Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome","volume":"149","author":"Yu","year":"2012","journal-title":"Cell"},{"key":"2023060106352524700_ref74","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1101\/gr.4362006","article-title":"Large-scale structure of genomic methylation patterns","volume":"16","author":"Rollins","year":"2006","journal-title":"Genome Res"},{"key":"2023060106352524700_ref75","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1093\/nar\/29.1.270","article-title":"MethDB\u2014a public database for DNA methylation data","volume":"29","author":"Grunau","year":"2001","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref76","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1016\/j.febslet.2005.07.002","article-title":"Prediction of methylated CpGs in DNA sequences using a support vector machine","volume":"579","author":"Bhasin","year":"2005","journal-title":"FEBS Lett"},{"key":"2023060106352524700_ref77","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1093\/bioinformatics\/btl377","article-title":"Predicting methylation status of CpG islands in the human brain","volume":"22","author":"Fang","year":"2006","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref78","doi-asserted-by":"crossref","first-page":"10713","DOI":"10.1073\/pnas.0602949103","article-title":"Computational prediction of methylation status in human genomic sequences","volume":"103","author":"Das","year":"2006","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref79","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/nmeth.3065","article-title":"Predicting the human epigenome from DNA motifs","volume":"12","author":"Whitaker","year":"2015","journal-title":"Nat Methods"},{"key":"2023060106352524700_ref80","doi-asserted-by":"crossref","first-page":"5868","DOI":"10.1093\/nar\/gki901","article-title":"Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis","volume":"33","author":"Meissner","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref81","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/nature08514","article-title":"Human DNA methylomes at base resolution show widespread epigenomic differences","volume":"462","author":"Lister","year":"2009","journal-title":"Nature"},{"key":"2023060106352524700_ref82","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1007\/s12561-016-9145-0","article-title":"Statistical challenges in analyzing methylation and long-range chromosomal interaction data","volume":"8","author":"Qin","year":"2016","journal-title":"Stat Biosci"},{"key":"2023060106352524700_ref83","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13059-017-1189-z","article-title":"DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning","volume":"18","author":"Angermueller","year":"2017","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref84","doi-asserted-by":"crossref","first-page":"e99","DOI":"10.1093\/nar\/gkx177","article-title":"Predicting the impact of non-coding variants on DNA methylation","volume":"45","author":"Zeng","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref85","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.ygeno.2016.02.005","article-title":"Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data","volume":"107","author":"Fan","year":"2016","journal-title":"Genomics"},{"key":"2023060106352524700_ref86","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13059-015-0581-9","article-title":"Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements","volume":"16","author":"Zhang","year":"2015","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref87","doi-asserted-by":"crossref","first-page":"19598","DOI":"10.1038\/srep19598","article-title":"Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks","volume":"6","author":"Wang","year":"2016","journal-title":"Sci Rep"},{"key":"2023060106352524700_ref88","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1186\/s12864-018-4766-y","article-title":"BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues","volume":"19","author":"Zou","year":"2018","journal-title":"BMC Genomics"},{"key":"2023060106352524700_ref89","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1126\/science.1067799","article-title":"Capturing chromosome conformation","volume":"295","author":"Dekker","year":"2002","journal-title":"Science"},{"key":"2023060106352524700_ref90","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1126\/science.1181369","article-title":"Comprehensive mapping of long-range interactions reveals folding principles of the human genome","volume":"326","author":"Lieberman-Aiden","year":"2009","journal-title":"Science"},{"key":"2023060106352524700_ref91","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nature12644","article-title":"A high-resolution map of the three-dimensional chromatin interactome in human cells","volume":"503","author":"Jin","year":"2013","journal-title":"Nature"},{"key":"2023060106352524700_ref92","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1038\/ng.3286","article-title":"Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C","volume":"47","author":"Mifsud","year":"2015","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref93","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s13059-015-0741-y","article-title":"Reconstructing A\/B compartments as revealed by Hi-C using long-range correlations in epigenetic data","volume":"16","author":"Fortin","year":"2015","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref94","doi-asserted-by":"crossref","first-page":"10812","DOI":"10.1038\/ncomms10812","article-title":"Constructing 3D interaction maps from 1D epigenomes","volume":"7","author":"Zhu","year":"2016","journal-title":"Nat Commun"},{"key":"2023060106352524700_ref95","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13059-015-0740-z","article-title":"Predicting chromatin organization using histone marks","volume":"16","author":"Huang","year":"2015","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref96","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13059-016-0909-0","article-title":"Predicting the three-dimensional folding of cis-regulatory regions in mammalian genomes using bioinformatic data and polymer models","volume":"17","author":"Brackley","year":"2016","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref97","doi-asserted-by":"crossref","first-page":"4660","DOI":"10.1038\/s41598-017-04929-6","article-title":"Prediction of chromatin accessibility in gene-regulatory regions from transcriptomics data","volume":"7","author":"Jung","year":"2017","journal-title":"Sci Rep"},{"key":"2023060106352524700_ref98","doi-asserted-by":"crossref","first-page":"E190","DOI":"10.1038\/35087138","article-title":"Navigating gene expression using microarrays\u2014a technology review","volume":"3","author":"Schulze","year":"2001","journal-title":"Nat Cell Biol"},{"key":"2023060106352524700_ref99","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/nmeth.1226","article-title":"Mapping and quantifying mammalian transcriptomes by RNA-Seq","volume":"5","author":"Mortazavi","year":"2008","journal-title":"Nat Methods"},{"key":"2023060106352524700_ref100","doi-asserted-by":"crossref","first-page":"15776","DOI":"10.1073\/pnas.2136655100","article-title":"Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage","volume":"100","author":"Shiraki","year":"2003","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref101","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/nmeth0306-211","article-title":"CAGE: cap analysis of gene expression","volume":"3","author":"Kodzius","year":"2006","journal-title":"Nat Methods"},{"key":"2023060106352524700_ref102","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1101\/gr.6018607","article-title":"Fusion transcripts and transcribed retrotransposed loci discovered through comprehensive transcriptome analysis using Paired-End diTags (PETs)","volume":"17","author":"Ruan","year":"2007","journal-title":"Genome Res"},{"key":"2023060106352524700_ref103","doi-asserted-by":"crossref","first-page":"e243","DOI":"10.1371\/journal.pcbi.0030243","article-title":"Predicting gene expression from sequence: a reexamination","volume":"3","author":"Yuan","year":"2007","journal-title":"PLoS Comput Biol"},{"key":"2023060106352524700_ref104","doi-asserted-by":"crossref","first-page":"2926","DOI":"10.1073\/pnas.0909344107","article-title":"Histone modification levels are predictive for gene expression","volume":"107","author":"Karlic","year":"2010","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023060106352524700_ref105","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1101\/gr.073080.107","article-title":"Inferring causal relationships among different histone modifications and gene expression","volume":"18","author":"Yu","year":"2008","journal-title":"Genome Res"},{"key":"2023060106352524700_ref106","doi-asserted-by":"crossref","first-page":"i639","DOI":"10.1093\/bioinformatics\/btw427","article-title":"DeepChrome: deep-learning for predicting gene expression from histone modifications","volume":"32","author":"Singh","year":"2016","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref107","doi-asserted-by":"crossref","first-page":"21521","DOI":"10.1073\/pnas.0904863106","article-title":"ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells","volume":"106","author":"Ouyang","year":"2009","journal-title":"Proc Natl Acad Sci USA"},{"issue":"Suppl 1","key":"2023060106352524700_ref108","doi-asserted-by":"crossref","first-page":"S50","DOI":"10.1186\/1471-2105-12-S1-S50","article-title":"A regression analysis of gene expression in ES cells reveals two gene classes that are significantly different in epigenetic patterns","volume":"12","author":"Park","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023060106352524700_ref109","doi-asserted-by":"crossref","first-page":"i405","DOI":"10.1093\/bioinformatics\/btw432","article-title":"Higher order methylation features for clustering and prediction in epigenomic studies","volume":"32","author":"Kapourani","year":"2016","journal-title":"Bioinformatics"},{"key":"2023060106352524700_ref110","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1101\/gr.135129.111","article-title":"Predicting cell-type-specific gene expression from regions of open chromatin","volume":"22","author":"Natarajan","year":"2012","journal-title":"Genome Res"},{"key":"2023060106352524700_ref111","doi-asserted-by":"crossref","first-page":"e120","DOI":"10.1093\/nar\/gkw446","article-title":"Quantitative modeling of gene expression using DNA shape features of binding sites","volume":"44","author":"Peng","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 1","key":"2023060106352524700_ref112","doi-asserted-by":"crossref","first-page":"S29","DOI":"10.1186\/1471-2105-12-S1-S29","article-title":"Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models","volume":"12","author":"Costa","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023060106352524700_ref113","doi-asserted-by":"crossref","first-page":"R15","DOI":"10.1186\/gb-2011-12-2-r15","article-title":"A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets","volume":"12","author":"Cheng","year":"2011","journal-title":"Genome Biol"},{"key":"2023060106352524700_ref114","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1093\/nar\/gkr752","article-title":"Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells","volume":"40","author":"Cheng","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023060106352524700_ref115","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1038\/ng.3367","article-title":"A gene-based association method for mapping traits using reference transcriptome data","volume":"47","author":"Gamazon","year":"2015","journal-title":"Nat Genet"},{"key":"2023060106352524700_ref116","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1038\/nbt.2203","article-title":"Absolute quantification of somatic DNA alterations in human cancer","volume":"30","author":"Carter","year":"2012","journal-title":"Nat Biotechnol"},{"key":"2023060106352524700_ref117","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13059-016-1143-5","article-title":"Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies","volume":"18","author":"Zheng","year":"2017","journal-title":"Genome Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/21\/1\/120\/50498351\/bby110.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/21\/1\/120\/50498351\/bby110.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T06:43:20Z","timestamp":1685601800000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/21\/1\/120\/5177808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,16]]},"references-count":117,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bby110","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,1]]},"published":{"date-parts":[[2018,11,16]]}}}